An Object Tracking Proposal Using Edge Computing
Abstract
The science and technology field has been advancing over time, and people surround themselves with computer systems, which help them in their routines. This paper presents the results of developing an approach to detect and track people at edge computing. The system runs on a Raspberry Pi 3B board and monitors the environment with the support of a camera. An interface was built that analyses the flow of people using a particular staircase. Information such as calories expended, effort, or activity time are offered to users in return for using the stairs.
Keywords:
Edge Computing, People Tracking, Caloric Calculations
References
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Raspberry Pi model description. https://www.raspberrypi.org/products/raspberry-pi-3-model-b/. Acesso: 20/03/2020.
Brisolara, L. and Matos, J. (2009). Desafios no Projeto de Sistemas Embarcados, pages 153–175.
Forsyth, D. A. and Ponce, J. (2002). Computer vision: a modern approach. Prentice Hall Professional Technical Reference.
Gubbi, J., Buyya, R., Marusic, S., and Palaniswami, M. (2013). Internet of things (iot): A vision, architectural elements, and future directions. Future generation computer systems, 29(7):1645–1660.
Heath, S. (2002). Embedded systems design. Elsevier.
Knappmeyer, M., Kiani, S. L., Reetz, E. S., Baker, N., and Tonjes, R. (2013). Sur- vey of context provisioning middleware. IEEE Communications Surveys & Tutorials, 15(3):1492–1519.
Lee, C.-C., Wang, C.-Y., Kao, C.-W., and Fan, K.-C. (2019). Real-time embedded system for human detection and tracking. In Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), pages 147–148. The Steering Committee of The World Congress in Computer Science, Computer.
Minichino, J. and Howse, J. (2020). Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning. Packt Publishing Ltd.
Nelson, D. L. and Cox, M. M. (2018). Princ ́ıpios de Bioqu ́ımica de Lehninger-7. Artmed Editora.
Østby, T. G. (2018). Object detection and tracking on a raspberry pi using background subtraction and convolutional neural networks. Master’s thesis, Universitetet i Sørøst- Norge.
Pimentel, V. and Nickerson, B. G. (2012). Communicating and displaying real-time data with websocket. IEEE Internet Computing, 16(4):45–53.
Remizov, A. (1991). Física médica y biológica. Mir.
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, pages 91–99.
Riener, R., Rabuffetti, M., and Frigo, C. (2002). Stair ascent and descent at different inclinations. Gait & posture, 15(1):32–44.
Rose, J. and GAMBLE, J. G. (1998). Marcha humana. São Paulo: Premier.
Tepljakov, A. (2015). Raspberry Pi based System for Visual Object Detection and Trac-king. PhD thesis, Bachelor’s Thesis.
Wolf, M. (2012). Computers as components: principles of embedded computing system design. Elsevier.
Published
2020-06-30
How to Cite
OLIVEIRA, Fernando; DELATORRE, Mateus; REINSTEIN, Heberth.
An Object Tracking Proposal Using Edge Computing. In: PROCEEDINGS OF BRAZILIAN SYMPOSIUM ON UBIQUITOUS AND PERVASIVE COMPUTING (SBCUP), 12. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 101-110.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2020.11216.
